Numerous pathologies have different histological phenotypes but similar radiographic appearances. In particular, breast cancer subtypes often have different histological phenotypes but similar radiographic appearances. These similar radiographic appearances may lead to difficulties in differentiating the different subtypes in a clinical environment. For example, the human epidermal growth factor receptor enriched (HER2-E) breast cancer subtype is difficult to distinguish from other subtypes of HER2 positive (HER2+) breast cancer when viewed with magnetic resonance imaging (MRI). HER2+ breast cancer is highly aggressive and insensitive to hormonal therapies. HER2+ breast cancer is also biologically and clinically heterogeneous. PAM50 gene profiling of HER2+ breast cancer identifies the HER2-E subtype as most responsive to HER2-targeted antibody therapy. However, conventional approaches to identifying HER2-E using PAM50 subtyping require expensive and invasive molecular profiling of breast cancer tissue.
Conventional methods for characterizing and distinguishing subtly different pathologies have employed analysis of texture features. However, conventional methods tend to capture global textural patterns. One conventional method that captures global textural patterns employs grey-level co-occurrence matrices (GLCM) and Gabor steerable features to compute global relationships between pixels by averaging responses to various filter operators within a neighborhood to a single global descriptor.
Another conventional approach to distinguishing subtly different pathologies employs local binary patterns (LBP) to provide a pixel-level response that can be used to generate a pixel-level or patch-based classification. Unlike GLCM, LBP provides a signature for every pixel by capturing localized intensity variations across the pixel. However, LBP is highly dependent on the radius parameter, which is critical when extracting local patterns. Additionally, both global and per-pixel texture representations are based on intensity variations and are domain agnostic. However, the histopathological differences between subtly different classes of HER2+ breast cancer may be manifested in differently oriented nuclei, lymphocytes, and glands. These differences in histopathological architecture, which are reflected in MRI imaging, are not reliably captured on a local scale by conventional methods like GLCM or LBP.